{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 权重的初始值\n",
    "在神经网络的学习中，权重的初始值特别重要。实际上，设定什么样的权重初始值，经常关系到神经网络的学习能否成功。本节将介绍权重初始值的推荐值，并通过实验确认神经网络的学习能否成功。\n",
    "\n",
    "#### 可以将权重初始值设为0吗\n",
    "结论是，将权重初始值设为0的话，将无法正确进行学习。\n",
    "\n",
    "为什么不能将权重初始值设为0呢？严格地说，**为什么不能将权重初始值设为一样的值呢？**\n",
    "\n",
    "这是因为在误差反向传播法中，所有的权重值都会进行相同的更新。权重被更新为相同的值，并拥有了对称的值(重复的值)。这使得神经网络拥有许多不同的权重的意义丧失了。为了防止“权重均一化”必须随机生成初始值。\n",
    "\n",
    "#### 隐藏层的激活值的分布\n",
    "观察隐藏层的激活值(激活函数的输出数据)的分布，可以获得很多启发。\n",
    "\n",
    "我们做一个实验：向一个5层神经网络(激活函数使用sigmoid函数)传入随机生成的输入数据，用直方图绘制各层激活值的数据分布。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coding: utf-8\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def sigmoid(x):\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "\n",
    "\n",
    "def ReLU(x):\n",
    "    return np.maximum(0, x)\n",
    "\n",
    "\n",
    "def tanh(x):\n",
    "    return np.tanh(x)\n",
    "\n",
    "input_data = np.random.randn(1000, 100)  # 1000个数据\n",
    "node_num = 100  # 各隐藏层的节点（神经元）数\n",
    "hidden_layer_size = 5  # 隐藏层有5层\n",
    "activations = {}  # 激活值的结果保存在这里\n",
    "\n",
    "x = input_data\n",
    "\n",
    "for i in range(hidden_layer_size):\n",
    "    if i != 0:\n",
    "        x = activations[i-1]\n",
    "\n",
    "    # 改变初始值进行实验！\n",
    "    w = np.random.randn(node_num, node_num) * 1\n",
    "    # w = np.random.randn(node_num, node_num) * 0.01\n",
    "    # w = np.random.randn(node_num, node_num) * np.sqrt(1.0 / node_num)\n",
    "    # w = np.random.randn(node_num, node_num) * np.sqrt(2.0 / node_num)\n",
    "\n",
    "\n",
    "    a = np.dot(x, w)\n",
    "\n",
    "\n",
    "    # 将激活函数的种类也改变，来进行实验！\n",
    "    z = sigmoid(a)\n",
    "    # z = ReLU(a)\n",
    "    # z = tanh(a)\n",
    "\n",
    "    activations[i] = z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里假设神经网络有5层，每层有100个神经元。用高斯分布随机生成1000个数据作为输入数据，并把它们传给5层神经网络。激活函数使用 sigmoid 函数，各层的激活值的结果保存在 activations 变量中。下面，我们画出 activations 中的各层数据的直方图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制直方图\n",
    "for i, a in activations.items():\n",
    "    plt.subplot(1, len(activations), i+1) # 绘制子图\n",
    "    plt.title(str(i+1) + \"-layer\")\n",
    "    if i != 0: plt.yticks([], [])\n",
    "    # plt.xlim(0.1, 1)\n",
    "    # plt.ylim(0, 7000)\n",
    "    plt.hist(a.flatten(), 30, range=(0,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由图可知，各层的激活值呈偏向0和1的分布。这里使用的是 sigmoid 函数是S型函数，随着输出不断地靠近0(或靠近1)，它的导数的值逐渐接近0。因此，偏向0和1的数据分布会造成反向传播中梯度的值不断变小，最后消失。这个问题称为**梯度消失**(gradient vanishing)。层次加深的深度学习中，梯度消失的问题可能会更加严重。\n",
    "\n",
    "下面，将权重的标准差设为0.01，进行相同的实验。实验代码只需要把设定的权重初始值修改即可。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "input_data = np.random.randn(1000, 100)  # 1000个数据\n",
    "node_num = 100  # 各隐藏层的节点（神经元）数\n",
    "hidden_layer_size = 5  # 隐藏层有5层\n",
    "activations = {}  # 激活值的结果保存在这里\n",
    "\n",
    "x = input_data\n",
    "\n",
    "for i in range(hidden_layer_size):\n",
    "    if i != 0:\n",
    "        x = activations[i-1]\n",
    "\n",
    "    # 改变初始值进行实验！\n",
    "    # w = np.random.randn(node_num, node_num) * 1\n",
    "    w = np.random.randn(node_num, node_num) * 0.01\n",
    "    # w = np.random.randn(node_num, node_num) * np.sqrt(1.0 / node_num)\n",
    "    # w = np.random.randn(node_num, node_num) * np.sqrt(2.0 / node_num)\n",
    "\n",
    "\n",
    "    a = np.dot(x, w)\n",
    "\n",
    "\n",
    "    # 将激活函数的种类也改变，来进行实验！\n",
    "    z = sigmoid(a)\n",
    "    # z = ReLU(a)\n",
    "    # z = tanh(a)\n",
    "\n",
    "    activations[i] = z\n",
    "    \n",
    "# 绘制直方图\n",
    "for i, a in activations.items():\n",
    "    plt.subplot(1, len(activations), i+1) # 绘制子图\n",
    "    plt.title(str(i+1) + \"-layer\")\n",
    "    if i != 0: plt.yticks([], [])\n",
    "    # plt.xlim(0.1, 1)\n",
    "    # plt.ylim(0, 7000)\n",
    "    plt.hist(a.flatten(), 30, range=(0,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用标准差为0.01的高斯分布时，各层的激活值的分布如图。这次呈集中在0.5附近的分布。因为不像刚才的例子那样偏向0和1，所以不会发生梯度消失的问题。但是，激活值的分布有所偏向，说明在表现力上会有很大问题。如果有多个神经元都输出几乎相同的值，那它们就没有存在的意义了。比如，如果100个神经元都输出几乎相同的值，那么也可以由1个神经元来表达基本相同的事情。因此，激活值在分布上有所偏向会出现“**表现力受限**”的问题。\n",
    "\n",
    "各层的激活值的分布都要求有适当的广度。为什么呢？因为通过在各层间传递多样性的数据，神经网络可以进行高效的学习。反过来，如果传递的是有所偏向的数据，就会出现梯度消失或者“表现力受限”的问题，导致学习可能无法顺利进行。\n",
    "\n",
    "\n",
    "接着，我们尝试使用 Xavier Glorot 等人的论文中推荐的权重初始值（俗称“Xavier初始值”）。现在，在一般的深度学习框架中，Xavier初始值已经被作为标准使用。Xavier 的论文中，为了使各层的激活值呈现出具有相同广度的分布，推导了合适的权重尺度。推导出的结论是，**如果前一层的节点数为n，则初始值使用标准为$\\frac{1}{\\sqrt{n}}$的分布。**"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)\n",
    "\n",
    "使用 Xavier 初始值后，前一层的节点数越多，要设定为目标节点的初始值的权重尺度就越小。现在，我们用 Xavier 初始值进行试验。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "input_data = np.random.randn(1000, 100)  # 1000个数据\n",
    "node_num = 100  # 各隐藏层的节点（神经元）数\n",
    "hidden_layer_size = 5  # 隐藏层有5层\n",
    "activations = {}  # 激活值的结果保存在这里\n",
    "\n",
    "x = input_data\n",
    "\n",
    "for i in range(hidden_layer_size):\n",
    "    if i != 0:\n",
    "        x = activations[i-1]\n",
    "\n",
    "    # 改变初始值进行实验！\n",
    "    # w = np.random.randn(node_num, node_num) * 1\n",
    "    # w = np.random.randn(node_num, node_num) * 0.01\n",
    "    w = np.random.randn(node_num, node_num) * np.sqrt(1.0 / node_num) # 使用Xavier初始值进行实验\n",
    "    # w = np.random.randn(node_num, node_num) * np.sqrt(2.0 / node_num)\n",
    "\n",
    "\n",
    "    a = np.dot(x, w)\n",
    "\n",
    "\n",
    "    # 将激活函数的种类也改变，来进行实验！\n",
    "    z = sigmoid(a)\n",
    "    # z = ReLU(a)\n",
    "    # z = tanh(a)\n",
    "\n",
    "    activations[i] = z\n",
    "    \n",
    "# 绘制直方图\n",
    "for i, a in activations.items():\n",
    "    plt.subplot(1, len(activations), i+1) # 绘制子图\n",
    "    plt.title(str(i+1) + \"-layer\")\n",
    "    if i != 0: plt.yticks([], [])\n",
    "    # plt.xlim(0.1, 1)\n",
    "    # plt.ylim(0, 7000)\n",
    "    plt.hist(a.flatten(), 30, range=(0,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用 Xavier 的初始值后的结果如图所示。从这个结果可知，越是后面的层，图像变得越歪斜，但是呈现了比之前更有广度的分布。因为各层间传递的数据有适当的广度，所以 sigmoid 函数的表现力不受限制，有望进行高效的学习。\n",
    "\n",
    "如果用 tanh函数(双曲线函数)代替 sigmoid 函数，这个稍微歪斜的问题就能得到改善。实际上，使用 tanh 函数后，会呈漂亮的吊钟型分布。tanh 函数和 sigmoid 函数同是S型曲线，但 tanh 是函数关于原点(0,0)兑成的S型曲线，而 sigmoid 函数是关于(x,y)=(0,0.5)对称的S型曲线。众所周知，**用作激活函数的函数最好具有关于原点对称的性质**。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ReLU 的权重初始值\n",
    "Xavier 初始值是**以激活函数是线性函数为前提**而推导出来的。因为 sigmoid 函数和 tanh 函数左右对称，且中央附近可以视作线性函数，所以适合使用 Xavier 初始值。但当激活函数使用 ReLU 时，一般推荐使用 ReLU 专用的初始值，也就是 Kaiming He 等人推荐的初始值，也成为“**He初始值**”。\n",
    "**当前一层的节点数为n时，He初始值使用标准差为$\\sqrt{\\frac{2}{n}}$的高斯分布**。当 Xavier 的初始值是$\\sqrt{1}{n}$时，（直观上）可以解释为，因为 ReLU 的负值区域的值为0，为了使它更有广度，所以需要2倍的系数。\n",
    "\n",
    "看一下激活函数使用 ReLU 时激活值的分布。给出了3个实验的结果，依次是权重初始值为标准差是0.01的高斯分布（简称\"std=0.01\"）、初始值为 Xavier初始值时、初始值为 ReLU专用的“He初始值”的结果。"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "观察实验结果可知。\n",
    "* 当“std = 0.01”时，各层的激活值都非常小。神经网络上传递的是非常小的值，说明逆向传播时权重的梯度也同样很小。这是很严重的问题，实际上学习基本没有进展。\n",
    "* 初始值为 Xavier 初始值时，随着层的加深，偏向一点点变大。实际上，层加深后，激活值的偏向变大，学习时会出现梯度消失的问题。\n",
    "* 当初始值为 He 初始值时，各层中分布的广度相同。由于即便随着层加深，数据的广度也能保持不变，因此逆向传播时，也会传递合适的值。\n",
    "\n",
    "总结一下，\n",
    "**当激活函数使用 ReLU 时，权重初始值使用 He 初始值；**\n",
    "\n",
    "**当激活函数使用 sigmoid 或 tanh 等S型函数时，初始值使用 Xavier 初始值。**\n",
    "\n",
    "这是目前的最佳实践。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 基于 MNIST 数据集的权重初始值的比较\n",
    "下面通过实际的数据，观察不同的权重初始值的赋值方法会在多大程度上影响神经网络的学习。\n",
    "\n",
    "这里，我们基于 std=0.01，Xavier初始值、He初始值进行试验。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===========iteration:0===========\n",
      "std=0.01:2.3025059346230474\n",
      "Xavier:2.2929558684965015\n",
      "He:2.263128983822529\n",
      "===========iteration:100===========\n",
      "std=0.01:2.3015649303305414\n",
      "Xavier:2.2442662190319025\n",
      "He:1.299110196328972\n",
      "===========iteration:200===========\n",
      "std=0.01:2.300617627050562\n",
      "Xavier:2.110598629019429\n",
      "He:0.4977616661103995\n",
      "===========iteration:300===========\n",
      "std=0.01:2.300021064808388\n",
      "Xavier:1.8434173664499909\n",
      "He:0.5472338690489869\n",
      "===========iteration:400===========\n",
      "std=0.01:2.3005997980235557\n",
      "Xavier:1.2172523250462328\n",
      "He:0.3242248949609189\n",
      "===========iteration:500===========\n",
      "std=0.01:2.302742051124217\n",
      "Xavier:0.8426766314773597\n",
      "He:0.3347976228502967\n",
      "===========iteration:600===========\n",
      "std=0.01:2.305674608356055\n",
      "Xavier:0.5316525490114328\n",
      "He:0.28098358684709623\n",
      "===========iteration:700===========\n",
      "std=0.01:2.3016508022170026\n",
      "Xavier:0.4840829874181871\n",
      "He:0.2485730592284734\n",
      "===========iteration:800===========\n",
      "std=0.01:2.2997677530640086\n",
      "Xavier:0.49227397058028777\n",
      "He:0.3263023521331945\n",
      "===========iteration:900===========\n",
      "std=0.01:2.3014330163023597\n",
      "Xavier:0.39965784662175174\n",
      "He:0.3246634426985251\n",
      "===========iteration:1000===========\n",
      "std=0.01:2.2986951296701568\n",
      "Xavier:0.2949578608507183\n",
      "He:0.19043296829648365\n",
      "===========iteration:1100===========\n",
      "std=0.01:2.3002523262579904\n",
      "Xavier:0.34821884954981075\n",
      "He:0.21549699271910477\n",
      "===========iteration:1200===========\n",
      "std=0.01:2.2970314768974576\n",
      "Xavier:0.44826107965923395\n",
      "He:0.3783904032739812\n",
      "===========iteration:1300===========\n",
      "std=0.01:2.3071157586352076\n",
      "Xavier:0.36909138157018223\n",
      "He:0.23023901515797812\n",
      "===========iteration:1400===========\n",
      "std=0.01:2.301427523116661\n",
      "Xavier:0.22258131259719\n",
      "He:0.17059529587936548\n",
      "===========iteration:1500===========\n",
      "std=0.01:2.2927764071478927\n",
      "Xavier:0.2560981884540364\n",
      "He:0.20039467491212148\n",
      "===========iteration:1600===========\n",
      "std=0.01:2.300736330595731\n",
      "Xavier:0.3933431809499339\n",
      "He:0.295639840281351\n",
      "===========iteration:1700===========\n",
      "std=0.01:2.306426639437036\n",
      "Xavier:0.3666122270359161\n",
      "He:0.1935900124894458\n",
      "===========iteration:1800===========\n",
      "std=0.01:2.3033432902924003\n",
      "Xavier:0.4087281547427633\n",
      "He:0.26690427047720156\n",
      "===========iteration:1900===========\n",
      "std=0.01:2.3003939141006406\n",
      "Xavier:0.21136787597404016\n",
      "He:0.13202308682366792\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# coding: utf-8\n",
    "import os\n",
    "import sys\n",
    "\n",
    "sys.path.append(os.pardir)  # 为了导入父目录的文件而进行的设定\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from dataset.mnist import load_mnist\n",
    "from common.util import smooth_curve\n",
    "from common.multi_layer_net import MultiLayerNet\n",
    "from common.optimizer import SGD\n",
    "\n",
    "\n",
    "# 0:读入MNIST数据==========\n",
    "(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True)\n",
    "\n",
    "train_size = x_train.shape[0]\n",
    "batch_size = 128\n",
    "max_iterations = 2000\n",
    "\n",
    "\n",
    "# 1:进行实验的设置==========\n",
    "weight_init_types = {'std=0.01': 0.01, 'Xavier': 'sigmoid', 'He': 'relu'}\n",
    "optimizer = SGD(lr=0.01)\n",
    "\n",
    "networks = {}\n",
    "train_loss = {}\n",
    "for key, weight_type in weight_init_types.items():\n",
    "    networks[key] = MultiLayerNet(input_size=784, hidden_size_list=[100, 100, 100, 100],\n",
    "                                  output_size=10, weight_init_std=weight_type)\n",
    "    train_loss[key] = []\n",
    "\n",
    "\n",
    "# 2:开始训练==========\n",
    "for i in range(max_iterations):\n",
    "    batch_mask = np.random.choice(train_size, batch_size)\n",
    "    x_batch = x_train[batch_mask]\n",
    "    t_batch = t_train[batch_mask]\n",
    "    \n",
    "    for key in weight_init_types.keys():\n",
    "        grads = networks[key].gradient(x_batch, t_batch)\n",
    "        optimizer.update(networks[key].params, grads)\n",
    "    \n",
    "        loss = networks[key].loss(x_batch, t_batch)\n",
    "        train_loss[key].append(loss)\n",
    "    \n",
    "    if i % 100 == 0:\n",
    "        print(\"===========\" + \"iteration:\" + str(i) + \"===========\")\n",
    "        for key in weight_init_types.keys():\n",
    "            loss = networks[key].loss(x_batch, t_batch)\n",
    "            print(key + \":\" + str(loss))\n",
    "\n",
    "\n",
    "# 3.绘制图形==========\n",
    "markers = {'std=0.01': 'o', 'Xavier': 's', 'He': 'D'}\n",
    "x = np.arange(max_iterations)\n",
    "for key in weight_init_types.keys():\n",
    "    plt.plot(x, smooth_curve(train_loss[key]), marker=markers[key], markevery=100, label=key)\n",
    "plt.xlabel(\"iterations\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.ylim(0, 2.5)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个实验中，神经网络有5层，每层有100个神经元，激活函数使用的是 ReLU。\n",
    "\n",
    "由图可知，std=0.01时完全无法进行学习。这和之前观察到的激活值的分布一样，是因为正向传播中传递的值很小（集中在0附近的数据）。因此，逆向传播时求到的梯度也很小，权重几乎不进行更新。相反，当权重初始值为 Xavier 初始值和 He 初始值时，学习进行的很顺利，并且，He 初始值时学习进度更快一些。\n",
    "\n",
    "综上，在神经网络的学习中，权重初始值非常重要。很多时候权重初始值的设定关系到神经网络的学习能否成功。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
